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README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+  - zh
+pretty_name: DECRO (eval)
+task_categories:
+  - audio-classification
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: "data/test-*.parquet"
+tags:
+  - anti-spoofing
+  - audio-deepfake-detection
+  - speech
+  - benchmark
+  - arena-ready
+arxiv:
+  - "10.1145/3543507.3583222"
+---
+
+# DECRO (eval)
+
+Benchmark-ready packaging of the **evaluation partition** of the **DECRO** (DEepfake CROss-lingual) dataset — a cross-lingual (English + Chinese) speech anti-spoofing / synthetic-voice detection benchmark from *Transferring Audio Deepfake Detection Capability across Languages* (TheWebConf / WWW 2023).
+
+## Overview
+
+DECRO pairs an English and a Chinese subset whose spoofed speech is generated with the **same** synthesis algorithms, so it isolates the effect of language on deepfake detection. The task is binary classification: **bonafide** (genuine human speech) vs. **spoof** (TTS / VC synthetic speech). This packaging contains the **eval** split of **both** language subsets, combined into a single test set. The original dataset is at https://github.com/petrichorwq/DECRO-dataset.
+
+## License & redistribution
+
+Redistributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license — the upstream DECRO license. See `LICENSE.txt`. CC BY 4.0 permits redistribution and derivative works with attribution. Labels and the evaluation protocol are unmodified; the audio was decoded and re-encoded to canonical 16 kHz mono FLAC (the source mixes sampling rates — 16 / 22.05 / 24 / 44.1 kHz — and a few FLOAT-subtype WAVs, so a uniform 16 kHz re-encode is required for evaluation).
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `path` | `string` | `<utterance_id>.flac`, unique |
+| `audio` | `Audio(16000)` | 16 kHz mono FLAC |
+| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
+| `notes` | `string` | JSON: `utterance_id`, `language`, `speaker_id`, `system_id` |
+
+`utterance_id` is the **language-prefixed** filename stem (`en_<stem>` / `ch_<stem>`); raw stems are not globally unique across the two subsets, so the prefix is the stable join key. `system_id` is the spoof algorithm (e.g. `hifigan`, `vits`, `baidu_en`) or, for bonafide, the source corpus (e.g. `asv19`, `aishell1`).
+
+`notes` example:
+```json
+{"utterance_id": "en_1-4993-40677-0048", "language": "en", "speaker_id": "1", "system_id": "baidu_en"}
+```
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("SpeechAntiSpoofingBenchmarks/DECRO", split="test")
+print(ds[0])
+```
+
+## Stats
+
+| Stat | Value |
+|------|-------|
+| Total trials | 37,314 |
+| Bonafide | 10,415 |
+| Spoof | 26,899 |
+| English (en_eval) | 19,190 (4,306 bonafide / 14,884 spoof) |
+| Chinese (ch_eval) | 18,124 (6,109 bonafide / 12,015 spoof) |
+
+## Source provenance
+
+- Original repository: https://github.com/petrichorwq/DECRO-dataset
+- Protocols: `en_eval.txt`, `ch_eval.txt` (format: `SPEAKER_ID AUDIO_FILE_NAME - SYSTEM_ID KEY`)
+- Bonafide: ASVspoof2019 LA (English); Aidatatang/Aishell/freeST/MagicData (Chinese). Spoof: WaveFake, FAD, and TTS/VC systems (Tacotron, FastSpeech2, VITS, StarGANv2-VC, NVC-Net, HiFiGAN, MB-MelGAN, PWG, Baidu, Xunfei).
+
+## Evaluation
+
+For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).
+
+## Citation
+
+```bibtex
+@inproceedings{ba2023transferring,
+  title     = {Transferring Audio Deepfake Detection Capability across Languages},
+  author    = {Ba, Zhongjie and Wen, Qing and Cheng, Peng and Wang, Yuwei and Lin, Feng and Lu, Li and Liu, Zhenguang},
+  booktitle = {Proceedings of the ACM Web Conference 2023 (WWW '23)},
+  year      = {2023},
+  doi       = {10.1145/3543507.3583222},
+}
+```
+
+## Maintainer
+
+Contact: k.n.borodin@mtuci.ru

build_parquet.py

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+"""Parquet build for the DECRO (eval) HF dataset repo.
+
+Reads the DECRO **eval** partitions of both language subsets -- English
+(``en_eval/`` + ``en_eval.txt``) and Chinese (``ch_eval/`` + ``ch_eval.txt``) --
+and emits the canonical 4-column schema (path / audio / label / notes) sharded
+into NUM_SHARDS parquet files.
+
+Always re-encode
+----------------
+The DECRO source audio is single-channel WAV but at *mixed* sampling rates
+(16 k / 22.05 k / 24 k / 44.1 k observed) with a handful of FLOAT-subtype WAVs.
+The arena requires a common 16 kHz SR and the HF ``datasets`` Audio decoder /
+validator / models all decode through soundfile, so we take the re-encode path
+unconditionally: Stage 1 loads EVERY clip with librosa (which also serves as the
+whole-set decodability probe) and writes a clean 16 kHz mono FLAC into a staging
+dir; Stage 2 assembles the shards from those clean files. librosa reads
+FLOAT-subtype WAV correctly (no int16 zero-out).
+
+Unique join key
+---------------
+Raw filename stems are NOT globally unique across the two subsets
+(``test_050_102`` / ``test_050_105`` exist in both en and ch). The immutable
+``utterance_id`` is therefore language-prefixed: ``{lang}_{stem}`` (lang in
+{en, ch}). ``path`` = ``{utterance_id}.flac``.
+
+Two stages, fully parallel, resumable. Stage 1 skips files already staged, so a
+killed run resumes at file granularity. The staging dir is gitignored.
+
+Sample mode (--limit N): first N rows into a single shard, skipping the
+full-count asserts -- used for the fast offline validate-dataset pass.
+"""
+
+import argparse
+import io
+import json
+import os
+import tempfile
+import warnings
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+
+# Mixed-rate source: librosa may fall back to audioread for some clips and emit
+# a PySoundFile UserWarning + librosa FutureWarning. Silence so the build log
+# stays readable.
+warnings.filterwarnings("ignore", message="PySoundFile failed")
+warnings.filterwarnings("ignore", category=FutureWarning, module="librosa")
+
+import datasets  # noqa: E402
+import librosa  # noqa: E402
+import pyarrow.parquet as pq  # noqa: E402
+import soundfile as sf  # noqa: E402
+from datasets import Audio, ClassLabel, Dataset, Features, Value  # noqa: E402
+from tqdm.auto import tqdm  # noqa: E402
+
+try:
+    datasets.disable_progress_bars()
+except AttributeError:
+    from datasets.utils.logging import disable_progress_bar
+
+    disable_progress_bar()
+
+REPO_ROOT = Path(__file__).resolve().parent
+SRC_ROOT = Path("/home/kirill/mnt/users_4tb/datasets/petrichorwq-DECRO-dataset-6fc9884")
+# (language, eval audio dir, eval protocol file)
+SPLITS = [
+    ("en", SRC_ROOT / "en_eval", SRC_ROOT / "en_eval.txt"),
+    ("ch", SRC_ROOT / "ch_eval", SRC_ROOT / "ch_eval.txt"),
+]
+PARQUET_DIR = REPO_ROOT / "data"
+STAGING_DIR = REPO_ROOT / "_clean_flac"
+NUM_SHARDS = 8
+EXPECTED_ROWS = 37314
+EXPECTED_BONAFIDE = 10415
+EXPECTED_SPOOF = 26899
+TARGET_SR = 16000
+WORKERS = int(os.environ.get("DECRO_BUILD_WORKERS", os.cpu_count() or 4))
+
+FEATURES = Features(
+    {
+        "path": Value("string"),
+        "audio": Audio(sampling_rate=16000),
+        "label": ClassLabel(names=["bonafide", "spoof"]),
+        "notes": Value("string"),
+    }
+)
+
+
+def parse_metadata():
+    """Parse both eval protocols.
+
+    Row format: ``SPEAKER_ID AUDIO_FILE_NAME - SYSTEM_ID KEY`` (col 3 unused).
+    Returns records with a language-prefixed unique ``uid``.
+    """
+    records = []
+    for lang, audio_dir, proto in SPLITS:
+        with open(proto, encoding="utf-8") as fh:
+            for line in fh:
+                line = line.strip()
+                if not line:
+                    continue
+                parts = line.split()
+                if len(parts) != 5:
+                    raise ValueError(f"Expected 5 columns, got {len(parts)}: {line!r}")
+                speaker, name, _unused, system_id, label = parts
+                if label not in ("bonafide", "spoof"):
+                    raise ValueError(f"Unexpected label {label!r}: {line!r}")
+                records.append(
+                    {
+                        "lang": lang,
+                        "name": name,
+                        "uid": f"{lang}_{name}",
+                        "src": audio_dir / f"{name}.wav",
+                        "speaker": speaker,
+                        "system_id": system_id,
+                        "label": label,
+                    }
+                )
+    return records
+
+
+def build_notes(rec):
+    return json.dumps(
+        {
+            "utterance_id": rec["uid"],
+            "language": rec["lang"],
+            "speaker_id": rec["speaker"],
+            "system_id": rec["system_id"],
+        }
+    )
+
+
+def _clip_duration(rec):
+    info = sf.info(str(rec["src"]))
+    return info.frames / info.samplerate
+
+
+def _ensure_long_first_row(records):
+    """Swap a clip with duration >= 1.0s to index 0 (validator D3 checks row 0)."""
+    for i in range(len(records)):
+        if _clip_duration(records[i]) >= 1.0:
+            if i != 0:
+                records[0], records[i] = records[i], records[0]
+            return
+    raise RuntimeError("No clip with duration >= 1.0s found")
+
+
+def _reencode_worker(task):
+    """Re-encode one source WAV into the staging dir. Resumable + atomic.
+
+    librosa decodes (handles mixed SR + FLOAT-subtype WAV correctly) and
+    resamples to 16 kHz mono; soundfile writes a clean FLAC. Writes to a temp
+    name then os.replace so a killed run never leaves a half-written file.
+    """
+    uid, src = task
+    dst = STAGING_DIR / f"{uid}.flac"
+    if dst.exists() and dst.stat().st_size > 0:
+        return (uid, None)
+    try:
+        y, _ = librosa.load(str(src), sr=TARGET_SR, mono=True)
+        if y.shape[0] == 0:
+            return (uid, "empty after decode")
+        tmp = STAGING_DIR / f".{uid}.flac.tmp"
+        sf.write(str(tmp), y, TARGET_SR, format="FLAC")
+        os.replace(tmp, dst)
+        return (uid, None)
+    except Exception as e:  # noqa: BLE001
+        return (uid, str(e).splitlines()[0][:120])
+
+
+def stage1_reencode(records):
+    """Re-encode all source WAV into STAGING_DIR across all cores (resumable).
+
+    This is also the whole-set decodability probe: any clip librosa cannot load
+    aborts the build.
+    """
+    STAGING_DIR.mkdir(parents=True, exist_ok=True)
+    tasks = [(r["uid"], r["src"]) for r in records]
+    total = len(tasks)
+    print(f"Stage 1: re-encoding {total} files with {WORKERS} workers -> {STAGING_DIR}")
+    failures = []
+    with ProcessPoolExecutor(max_workers=WORKERS) as ex:
+        for uid, err in tqdm(
+            ex.map(_reencode_worker, tasks, chunksize=64),
+            total=total,
+            desc="Stage 1 re-encode",
+            unit="file",
+        ):
+            if err:
+                failures.append((uid, err))
+    print(f"Stage 1 done: {total} processed, {len(failures)} failures")
+    if failures:
+        for uid, err in failures[:10]:
+            print(f"  FAIL {uid}: {err}")
+        raise RuntimeError(f"{len(failures)} files failed to re-encode")
+
+
+def _build_shard(task):
+    """Worker: build one shard parquet from its row slice. Resumable + atomic."""
+    shard_index, rows, num_shards = task
+    shard_name = f"test-{shard_index:05d}-of-{num_shards:05d}.parquet"
+    final = PARQUET_DIR / shard_name
+    if final.exists() and final.stat().st_size > 0:
+        return (shard_index, len(rows), "skipped")
+
+    def row_gen():
+        for rec in rows:
+            uid = rec["uid"]
+            yield {
+                "path": f"{uid}.flac",
+                "audio": {
+                    "bytes": (STAGING_DIR / f"{uid}.flac").read_bytes(),
+                    "path": f"{uid}.flac",
+                },
+                "label": rec["label"],
+                "notes": build_notes(rec),
+            }
+
+    with tempfile.TemporaryDirectory() as cache:
+        ds = Dataset.from_generator(row_gen, features=FEATURES, cache_dir=cache)
+        tmp = PARQUET_DIR / f".{shard_name}.tmp"
+        ds.to_parquet(str(tmp))
+        os.replace(tmp, final)
+    return (shard_index, len(rows), "built")
+
+
+def _partition(records, num_shards):
+    n = len(records)
+    per = (n + num_shards - 1) // num_shards
+    out = []
+    for i in range(num_shards):
+        chunk = records[i * per : (i + 1) * per]
+        if chunk:
+            out.append(chunk)
+    return out
+
+
+def build():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--limit", type=int, default=None)
+    args = parser.parse_args()
+    limit = args.limit
+    sample_mode = limit is not None
+
+    print("Reading metadata from", ", ".join(str(p) for _, _, p in SPLITS))
+    records = parse_metadata()
+    print(f"Parsed {len(records)} rows")
+    if not sample_mode:
+        assert len(records) == EXPECTED_ROWS, f"Expected {EXPECTED_ROWS}, got {len(records)}"
+
+    check = records if not sample_mode else records[: max(limit * 4, limit)]
+    missing = [r["uid"] for r in check if not r["src"].exists()]
+    assert not missing, f"{len(missing)} wav files missing, e.g. {missing[:5]}"
+
+    records.sort(key=lambda r: r["uid"])
+    _ensure_long_first_row(records)
+
+    if sample_mode:
+        records = records[:limit]
+        num_shards = 1
+        print(f"SAMPLE MODE: {len(records)} rows -> 1 shard")
+    else:
+        num_shards = NUM_SHARDS
+
+    bona = sum(1 for r in records if r["label"] == "bonafide")
+    spoof = sum(1 for r in records if r["label"] == "spoof")
+    print(f"  bonafide={bona} spoof={spoof} total={len(records)}")
+
+    # Stage 1: parallel re-encode to the staging dir (resumable + whole-set probe).
+    stage1_reencode(records)
+
+    # Stage 2: assemble parquet shards (one worker per shard).
+    PARQUET_DIR.mkdir(parents=True, exist_ok=True)
+    keep_suffix = f"-of-{num_shards:05d}.parquet"
+    for stale in PARQUET_DIR.glob("test-*.parquet"):
+        if not stale.name.endswith(keep_suffix):
+            print(f"Removing stale shard {stale.name}")
+            stale.unlink()
+    shards = _partition(records, num_shards)
+    tasks = [(i, rows, num_shards) for i, rows in enumerate(shards)]
+    stage2_workers = min(WORKERS, len(tasks))
+    print(f"Stage 2: building {len(tasks)} shard(s) with {stage2_workers} workers...")
+    built = skipped = 0
+    with ProcessPoolExecutor(max_workers=stage2_workers) as ex:
+        for idx, n, status in tqdm(
+            ex.map(_build_shard, tasks),
+            total=len(tasks),
+            desc="Stage 2 shards",
+            unit="shard",
+        ):
+            if status == "built":
+                built += 1
+            elif status == "skipped":
+                skipped += 1
+    print(f"Stage 2 done: {built} built, {skipped} skipped")
+
+    _verify(num_shards, sample_mode)
+    print("All verifications passed!")
+
+    if not sample_mode:
+        from speech_spoof_bench import labels
+
+        out = labels.emit_labels(REPO_ROOT)
+        print(f"Wrote {out}")
+
+
+def _verify(num_shards, sample_mode):
+    shards = sorted(PARQUET_DIR.glob("test-*.parquet"))
+    total = sum(pq.read_metadata(str(f)).num_rows for f in shards)
+    uid_set, path_set, bona, spoof = set(), set(), 0, 0
+    for f in shards:
+        t = pq.read_table(str(f), columns=["path", "label", "notes"])
+        for p, lab, n in zip(
+            t.column("path").to_pylist(),
+            t.column("label").to_pylist(),
+            t.column("notes").to_pylist(),
+        ):
+            path_set.add(p)
+            uid_set.add(json.loads(n)["utterance_id"])
+            if lab == 0:
+                bona += 1
+            elif lab == 1:
+                spoof += 1
+    assert len(uid_set) == total, "Duplicate utterance_ids"
+    assert len(path_set) == total, "Duplicate paths"
+    if not sample_mode:
+        assert total == EXPECTED_ROWS, f"{total} != {EXPECTED_ROWS}"
+        assert bona == EXPECTED_BONAFIDE, f"bonafide {bona} != {EXPECTED_BONAFIDE}"
+        assert spoof == EXPECTED_SPOOF, f"spoof {spoof} != {EXPECTED_SPOOF}"
+    t0 = pq.read_table(str(shards[0]))
+    assert set(t0.column_names) == {"path", "audio", "label", "notes"}, t0.column_names
+    audio0 = t0.column("audio")[0].as_py()
+    data, sr = sf.read(io.BytesIO(audio0["bytes"]))
+    dur = len(data) / sr
+    assert sr == 16000, f"row0 sr {sr} != 16000"
+    assert dur >= 1.0, f"row0 dur {dur:.2f}s < 1.0s"
+    print(f"  verify: {total} rows, row0 {sr}Hz {dur:.2f}s decodable OK")
+
+
+if __name__ == "__main__":
+    build()

data/labels.parquet

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eval.yaml

@@ -0,0 +1,25 @@
+name: DECRO
+description: >
+  Evaluation partition of the DECRO (DEepfake CROss-lingual) dataset, English +
+  Chinese subsets combined. Binary classification: bonafide vs. spoof, where the
+  two languages share the same set of TTS/VC synthesis algorithms. EER computed
+  on the combined eval protocol (37,314 utterances).
+evaluation_framework: inspect-ai
+
+tasks:
+  - id: antispoofing_eval
+    config: default
+    split: test
+
+    field_spec:
+      input: audio
+      target: label
+
+    solvers:
+      - name: speech_spoof_bench_solver
+
+    scorers:
+      - name: speech_spoof_scorer
+
+    metrics:
+      - eer_percent

push.py

@@ -0,0 +1,38 @@
+"""Push the built DECRO dataset to the HF Hub (resumable, proxy-free).
+
+Run with the network proxy unset for the >1 GB shard upload:
+  env -u HTTP_PROXY -u HTTPS_PROXY -u http_proxy -u https_proxy python push.py
+"""
+
+from pathlib import Path
+
+from huggingface_hub import HfApi
+
+REPO_ID = "SpeechAntiSpoofingBenchmarks/DECRO"
+FOLDER = Path(__file__).resolve().parent
+
+IGNORE = [
+    "_clean_flac/**",
+    "__pycache__/**",
+    "*.pyc",
+    ".cache/**",
+    ".git/**",
+    ".gitignore",
+]
+
+
+def main():
+    api = HfApi()
+    api.create_repo(REPO_ID, repo_type="dataset", exist_ok=True)
+    api.upload_large_folder(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        folder_path=str(FOLDER),
+        ignore_patterns=IGNORE,
+    )
+    info = api.dataset_info(REPO_ID)
+    print("Pushed. Latest commit SHA:", info.sha)
+
+
+if __name__ == "__main__":
+    main()

submissions/README.md

@@ -0,0 +1,81 @@
+# Benchmark Submissions
+
+To submit a result, you'll upload two files (no git clone required):
+
+1. **`scores.txt`** to **your own HF model repo** under `.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt`.
+2. **`<your-slug>.yaml`** as a pull request to **this dataset repo** under `submissions/<your-slug>.yaml`.
+
+The YAML in this repo carries a pinned URL pointing at your `scores.txt`, plus its sha256. Scores files do not live in this repo.
+
+## Submitter workflow
+
+### 1. Generate `scores.txt` locally
+
+```bash
+speech-spoof-bench run \
+  --model-module <your_package>:<YourModelClass> \
+  --datasets SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA
+```
+
+Output: `results/ASVspoof2019_LA/scores.txt` (one line per utterance, `<utterance_id> <score>`, higher = more bonafide).
+
+### 2. Upload `scores.txt` to your model repo
+
+```bash
+huggingface-cli upload <your-owner>/<your-model-repo> \
+  results/ASVspoof2019_LA/scores.txt \
+  .eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt \
+  --repo-type=model \
+  --commit-message="Add ASVspoof2019_LA scores"
+```
+
+**Note the commit sha** the CLI prints — you'll need it in the next step.
+
+### 3. Fill in the submission YAML
+
+Copy `results_template.yaml` to `<your-slug>.yaml` and fill in every field. The two most important fields:
+
+- `artifact.scores_url`: the **pinned** URL to your uploaded scores file. Use the commit sha from step 2, not `main`:
+  ```
+  https://huggingface.co/<your-owner>/<your-model-repo>/resolve/<commit-sha-from-step-2>/.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt
+  ```
+  URLs with `/resolve/main/` are rejected because they're mutable.
+- `artifact.scores_sha256`: `sha256sum results/ASVspoof2019_LA/scores.txt | awk '{print $1}'`.
+
+Leave the `reproduction:` block empty — the maintainer fills it in at merge time.
+
+### 4. Open the PR via HF CLI
+
+```bash
+huggingface-cli upload \
+  SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA \
+  <your-slug>.yaml submissions/<your-slug>.yaml \
+  --repo-type=dataset \
+  --create-pr \
+  --commit-message="Add <your-slug> submission"
+```
+
+The CLI prints a PR URL. That's it.
+
+### 5. Wait for maintainer reproduction
+
+A maintainer runs `speech-spoof-bench reproduce --scoring <PR-branch>`, which:
+- Fetches `scores_url`.
+- Verifies the sha256 against `artifact.scores_sha256`.
+- Recomputes EER from the file.
+- Compares to your claimed `scores.eer_percent` (must match within 1e-6).
+
+If it passes, the maintainer fills in `reproduction:` and merges. If it fails, you get a comment on the PR explaining why.
+
+## Verification levels
+
+| Level | What the maintainer checks | Cost |
+|---|---|---|
+| `scoring` (default) | sha + recomputed EER from your `scores.txt`. | Seconds. |
+| `inference` (optional, follow-up) | Re-runs your checkpoint end-to-end and regenerates `scores.txt`. Must match within 0.05% EER. | Expensive. |
+
+Submissions without a `reproduction:` block never appear in the arena.
+
+## What about git clone + push?
+
+You can do it that way too, but for a single 2 KB YAML it's massively heavier. The HF CLI path is the documented one.

submissions/results_template.yaml

@@ -0,0 +1,44 @@
+schema_version: 4
+
+system:
+  name: ""
+  slug: ""
+  description: ""
+  code: ""
+  checkpoint: ""
+  paper:
+    arxiv_id: ""
+    url: ""
+    bibtex: |
+      @article{...}
+
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA
+  revision: ""
+  split: test
+
+scores:
+  eer_percent: 0.0
+  n_trials: 71237
+  n_skipped: 0
+
+artifact:
+  # Must be pinned by commit sha. Pattern:
+  #   https://huggingface.co/<owner>/<repo>/resolve/<commit-sha>/.eval_results/SpeechAntiSpoofingBenchmarks/ASVspoof2019_LA/scores.txt
+  scores_url: ""
+  scores_sha256: ""
+  bench_version: ""
+
+# Leave this block empty — the maintainer fills it in at merge.
+reproduction:
+  reproduced_by: ""
+  reproduced_at: ""
+  reproduced_bench_version: ""
+  match: ""
+
+submitter:
+  hf_username: ""
+  contact: ""
+
+submitted_at: ""
+notes: ""